{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/dux/NeuralForceField/models\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import logging\n",
    "import os\n",
    "import pickle\n",
    "from pathlib import Path\n",
    "from time import perf_counter\n",
    "\n",
    "import numpy as np\n",
    "from nff.io.ase_calcs import NeuralFF\n",
    "\n",
    "from mcmc import MCMC\n",
    "from mcmc.system import SurfaceSystem\n",
    "from mcmc.utils import setup_logger\n",
    "from mcmc.utils.misc import get_atoms_batch\n",
    "from mcmc.utils.plot import plot_summary_stats\n",
    "\n",
    "np.set_printoptions(precision=3, suppress=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize pristine slab and parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize paths\n",
    "surface_name = \"SrTiO3_001\"\n",
    "run_folder = Path() / surface_name\n",
    "run_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Initialize logger\n",
    "logger = setup_logger(\"mcmc\", run_folder / \"mc.log\", logging.INFO)\n",
    "logging.getLogger(\n",
    "    \"matplotlib.font_manager\"\n",
    ").disabled = True  # disable matplotlib font manager logging\n",
    "\n",
    "# Load prepared pristine slab\n",
    "try:\n",
    "    with open(\"data/SrTiO3_001/SrTiO3_001_2x2_pristine_slab.pkl\", \"rb\") as f:\n",
    "        slab = pickle.load(f)\n",
    "except FileNotFoundError as e:\n",
    "    print(\"Pristine surface pkl file not found. Please check you have downloaded the data.\")\n",
    "    raise e\n",
    "offset_data_path = os.path.join(\n",
    "    os.getcwd(),\n",
    "    \"data/SrTiO3_001/nff\",\n",
    "    \"offset_data.json\",\n",
    ")\n",
    "\n",
    "try:\n",
    "    with open(offset_data_path, \"r\") as f:\n",
    "        offset_data = json.load(f)\n",
    "except FileNotFoundError as e:\n",
    "    print(\"Offset data file not found. Please check you have downloaded the data.\")\n",
    "    raise e\n",
    "\n",
    "calc_settings = {\n",
    "    \"calc_name\": \"NFF\",\n",
    "    \"optimizer\": \"BFGS\",\n",
    "    \"chem_pots\": {\"Sr\": -2, \"Ti\": 0, \"O\": 0},\n",
    "    \"relax_atoms\": True,\n",
    "    \"relax_steps\": 20,\n",
    "    \"offset\": True,\n",
    "    \"offset_data\": offset_data,\n",
    "}\n",
    "\n",
    "system_settings = {\n",
    "    \"surface_name\": surface_name,\n",
    "    \"surface_depth\": 1,\n",
    "    \"cutoff\": 5.0,\n",
    "    \"near_reduce\": 0.01,\n",
    "    \"planar_distance\": 1.5,\n",
    "    \"no_obtuse_hollow\": True,\n",
    "    \"ads_site_type\": \"all\",\n",
    "}\n",
    "\n",
    "sampling_settings = {\n",
    "    \"total_sweeps\": 10,\n",
    "    \"sweep_size\": 5,\n",
    "    \"start_temp\": 1.0,  # in terms of kbT\n",
    "    \"perform_annealing\": False,\n",
    "    \"alpha\": 1.0,  # no annealing\n",
    "    \"adsorbates\": list(calc_settings[\"chem_pots\"].keys()),\n",
    "    \"run_folder\": run_folder,\n",
    "}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up NFF Calculator. Here, we are using the same neural network weights from our Zenodo dataset (https://zenodo.org/record/7927039). The ensemble requires an `offset_data.json` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:00 - mcmc.calculators | INFO: chemical potentials: {'Sr': -2, 'Ti': 0, 'O': 0} are set from parameters\n",
      "10:07:00 - mcmc.calculators | INFO: offset data: {'bulk_energies': {'O': -0.17747231201, 'Sr': -0.06043637668, 'SrTiO3': -1.470008697358702}, 'stoidict': {'Sr': 0.49995161381315867, 'Ti': -0.0637500349111578, 'O': -0.31241304903276834, 'offset': -11.324476454433157}, 'stoics': {'Sr': 1, 'Ti': 1, 'O': 3}, 'ref_formula': 'SrTiO3', 'ref_element': 'Ti'} is set from parameters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "offset data: {'bulk_energies': {'O': -0.17747231201, 'Sr': -0.06043637668, 'SrTiO3': -1.470008697358702}, 'stoidict': {'Sr': 0.49995161381315867, 'Ti': -0.0637500349111578, 'O': -0.31241304903276834, 'offset': -11.324476454433157}, 'stoics': {'Sr': 1, 'Ti': 1, 'O': 3}, 'ref_formula': 'SrTiO3', 'ref_element': 'Ti'} is set from parameters\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'calc_name': 'NFF',\n",
       " 'optimizer': 'BFGS',\n",
       " 'chem_pots': {'Sr': -2, 'Ti': 0, 'O': 0},\n",
       " 'relax_atoms': True,\n",
       " 'relax_steps': 20,\n",
       " 'offset': True,\n",
       " 'offset_data': {'bulk_energies': {'O': -0.17747231201,\n",
       "   'Sr': -0.06043637668,\n",
       "   'SrTiO3': -1.470008697358702},\n",
       "  'stoidict': {'Sr': 0.49995161381315867,\n",
       "   'Ti': -0.0637500349111578,\n",
       "   'O': -0.31241304903276834,\n",
       "   'offset': -11.324476454433157},\n",
       "  'stoics': {'Sr': 1, 'Ti': 1, 'O': 3},\n",
       "  'ref_formula': 'SrTiO3',\n",
       "  'ref_element': 'Ti'}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from nff.utils.cuda import cuda_devices_sorted_by_free_mem\n",
    "\n",
    "from mcmc.calculators import EnsembleNFFSurface\n",
    "\n",
    "DEVICE = f\"cuda:{cuda_devices_sorted_by_free_mem()[-1]}\" if torch.cuda.is_available() else \"cpu\"\n",
    "\n",
    "# requires an ensemble of models in this path and an `offset_data.json` file\n",
    "nnids = [\"model01\", \"model02\", \"model03\"]\n",
    "model_dirs = [\n",
    "    os.path.join(\n",
    "        os.getcwd(),\n",
    "        \"data/SrTiO3_001/nff\",\n",
    "        str(x),\n",
    "        \"best_model\",\n",
    "    )\n",
    "    for x in nnids\n",
    "]\n",
    "\n",
    "models = []\n",
    "for modeldir in model_dirs:\n",
    "    m = NeuralFF.from_file(modeldir, device=DEVICE).model\n",
    "    models.append(m)\n",
    "\n",
    "nff_surf_calc = EnsembleNFFSurface(models, device=DEVICE)\n",
    "nff_surf_calc.set(**calc_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize surface system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:00 - mcmc.system | INFO: Initalizing adsorption sites with settings: {'surface_name': 'SrTiO3_001', 'surface_depth': 1, 'cutoff': 5.0, 'near_reduce': 0.01, 'planar_distance': 1.5, 'no_obtuse_hollow': True, 'ads_site_type': 'all'}\n",
      "10:07:00 - mcmc.system | INFO: Generated adsorption coordinates are: [array([ 7.871,  7.941, 18.82 ]), array([ 1.968,  1.951, 18.779]), array([ 1.968,  0.146, 18.732]), array([ 7.871,  3.956, 18.82 ]), array([ 1.968,  5.936, 18.779])]...\n",
      "10:07:00 - mcmc.system | INFO: Initializing 64 virtual atoms\n",
      "10:07:00 - mcmc.system | INFO: Initial state is [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "10:07:00 - mcmc.system | INFO: Number of pristine atoms is 60\n",
      "10:07:00 - mcmc.system | INFO: Bulk indices are [ 0  1  2  3  4  5  6  9 10 11 12 13 14 15 16 17 18 19 20 21 24 25 26 27\n",
      " 28 29 30 31 32 33 34 35 36 39 40 41 42 43 44 45 46 47 48 49 50 51 54 55\n",
      " 56 57 58 59]\n",
      "10:07:00 - mcmc.system | INFO: Surface indices are [ 7  8 22 23 37 38 52 53]\n",
      "10:07:00 - mcmc.system | INFO: Constraints are FixAtoms(indices=[0, 1, 2, 3, 4, 5, 6, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 54, 55, 56, 57, 58, 59])\n",
      "10:07:00 - mcmc.system | INFO: Relaxing initialized surface\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:02     -467.521881        0.204407\n",
      "BFGS:    1 10:07:02     -467.525757        0.180273\n",
      "BFGS:    2 10:07:02     -467.540619        0.079328\n",
      "BFGS:    3 10:07:02     -467.540924        0.059823\n",
      "BFGS:    4 10:07:02     -467.541351        0.005870\n"
     ]
    }
   ],
   "source": [
    "slab_batch = get_atoms_batch(\n",
    "    slab,\n",
    "    system_settings[\"cutoff\"],\n",
    "    DEVICE,\n",
    "    props={\"energy\": 0, \"energy_grad\": []},\n",
    ")\n",
    "\n",
    "surface = SurfaceSystem(\n",
    "    slab_batch,\n",
    "    calc=nff_surf_calc,\n",
    "    system_settings=system_settings,\n",
    "    save_folder=run_folder,\n",
    ")\n",
    "surface.all_atoms.write(run_folder / \"SrTiO3_001_2x2_all_virtual_ads.cif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate pristine surface energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Surface energy 12.471 eV\n"
     ]
    }
   ],
   "source": [
    "print(f\"Surface energy {float(surface.get_surface_energy()):.3f} eV\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform MCMC and view results. Detailed results can be found in the corresponding run in the `SrTiO3(001)/` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:03 - mcmc.mcmc | INFO: Using run folder SrTiO3_001\n",
      "10:07:03 - mcmc.mcmc | INFO: There are 60 atoms in pristine slab\n",
      "10:07:03 - mcmc.mcmc | INFO: Running with total_sweeps = 10, sweep_size = 5, start_temp = 1.000\n",
      "10:07:03 - mcmc.mcmc | INFO: Starting with iteration 0\n",
      "10:07:03 - mcmc.mcmc | INFO: Temperature schedule is: ['1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000', '1.000']\n",
      "10:07:03 - mcmc.mcmc | INFO: In sweep 1 out of 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:03     -468.047943       13.644413\n",
      "BFGS:    1 10:07:03     -469.902222        4.257286\n",
      "BFGS:    2 10:07:03     -470.302765        1.848279\n",
      "BFGS:    3 10:07:03     -470.448517        0.937589\n",
      "BFGS:    4 10:07:03     -470.502319        0.784208\n",
      "BFGS:    5 10:07:04     -470.572266        0.435066\n",
      "BFGS:    6 10:07:04     -470.611481        0.449032\n",
      "BFGS:    7 10:07:04     -470.628204        0.671624\n",
      "BFGS:    8 10:07:04     -470.381256        2.700147\n",
      "BFGS:    9 10:07:04     -470.649261        0.671251\n",
      "BFGS:   10 10:07:05     -470.659332        0.641276\n",
      "BFGS:   11 10:07:05     -470.691010        0.529939\n",
      "BFGS:   12 10:07:05     -470.701172        0.464180\n",
      "BFGS:   13 10:07:05     -470.724457        0.270366\n",
      "BFGS:   14 10:07:06     -470.727051        0.137828\n",
      "BFGS:   15 10:07:06     -470.729492        0.117855\n",
      "BFGS:   16 10:07:06     -470.730804        0.132777\n",
      "BFGS:   17 10:07:06     -470.731598        0.110335\n",
      "BFGS:   18 10:07:06     -470.732086        0.059124\n",
      "BFGS:   19 10:07:06     -470.732330        0.053406\n",
      "BFGS:   20 10:07:06     -470.732544        0.030161\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:07     -459.404266       43.275435\n",
      "BFGS:    1 10:07:07     -466.912506        9.479544\n",
      "BFGS:    2 10:07:07     -467.658112        6.733044\n",
      "BFGS:    3 10:07:07     -468.662567        2.171854\n",
      "BFGS:    4 10:07:08     -468.910797        0.925837\n",
      "BFGS:    5 10:07:08     -469.058258        1.242999\n",
      "BFGS:    6 10:07:08     -469.096100        1.131081\n",
      "BFGS:    7 10:07:08     -469.153839        0.849155\n",
      "BFGS:    8 10:07:08     -469.186371        0.649674\n",
      "BFGS:    9 10:07:08     -469.210327        0.566827\n",
      "BFGS:   10 10:07:09     -469.231567        0.456605\n",
      "BFGS:   11 10:07:09     -469.245392        0.291022\n",
      "BFGS:   12 10:07:09     -469.251099        0.257445\n",
      "BFGS:   13 10:07:09     -469.254974        0.181047\n",
      "BFGS:   14 10:07:09     -469.258881        0.122074\n",
      "BFGS:   15 10:07:09     -469.261932        0.150921\n",
      "BFGS:   16 10:07:09     -469.263672        0.159587\n",
      "BFGS:   17 10:07:10     -469.265778        0.139941\n",
      "BFGS:   18 10:07:10     -469.268097        0.133592\n",
      "BFGS:   19 10:07:10     -469.270172        0.103957\n",
      "BFGS:   20 10:07:11     -469.271393        0.060983\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:11     -468.047943       13.644409\n",
      "BFGS:    1 10:07:11     -469.902222        4.257288\n",
      "BFGS:    2 10:07:11     -470.302765        1.848278\n",
      "BFGS:    3 10:07:11     -470.448517        0.937591\n",
      "BFGS:    4 10:07:12     -470.502319        0.784211\n",
      "BFGS:    5 10:07:12     -470.572266        0.435063\n",
      "BFGS:    6 10:07:12     -470.611481        0.449028\n",
      "BFGS:    7 10:07:12     -470.628204        0.671617\n",
      "BFGS:    8 10:07:12     -470.381256        2.700148\n",
      "BFGS:    9 10:07:12     -470.649261        0.671250\n",
      "BFGS:   10 10:07:13     -470.659332        0.641271\n",
      "BFGS:   11 10:07:13     -470.691010        0.529981\n",
      "BFGS:   12 10:07:13     -470.701172        0.464138\n",
      "BFGS:   13 10:07:13     -470.724396        0.270353\n",
      "BFGS:   14 10:07:13     -470.727051        0.137811\n",
      "BFGS:   15 10:07:14     -470.729492        0.117881\n",
      "BFGS:   16 10:07:14     -470.730804        0.132780\n",
      "BFGS:   17 10:07:14     -470.731598        0.110282\n",
      "BFGS:   18 10:07:14     -470.732086        0.059136\n",
      "BFGS:   19 10:07:14     -470.732330        0.053431\n",
      "BFGS:   20 10:07:14     -470.732513        0.030156\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:15     -472.107758       14.827252\n",
      "BFGS:    1 10:07:15     -474.982666        5.282108\n",
      "BFGS:    2 10:07:15     -475.946381        3.643173\n",
      "BFGS:    3 10:07:16     -476.488434        2.438390\n",
      "BFGS:    4 10:07:16     -476.900269        2.096166\n",
      "BFGS:    5 10:07:17     -477.321777        1.649354\n",
      "BFGS:    6 10:07:17     -477.499664        1.468398\n",
      "BFGS:    7 10:07:17     -477.621460        0.938416\n",
      "BFGS:    8 10:07:17     -477.683594        0.638236\n",
      "BFGS:    9 10:07:18     -477.713776        0.599719\n",
      "BFGS:   10 10:07:18     -477.795746        0.836607\n",
      "BFGS:   11 10:07:18     -477.811127        0.493398\n",
      "BFGS:   12 10:07:18     -477.830811        0.259688\n",
      "BFGS:   13 10:07:18     -477.835541        0.154513\n",
      "BFGS:   14 10:07:19     -477.837646        0.121719\n",
      "BFGS:   15 10:07:19     -477.838654        0.107016\n",
      "BFGS:   16 10:07:19     -477.839966        0.099565\n",
      "BFGS:   17 10:07:19     -477.840851        0.103740\n",
      "BFGS:   18 10:07:19     -477.841766        0.089933\n",
      "BFGS:   19 10:07:19     -477.842377        0.064538\n",
      "BFGS:   20 10:07:19     -477.842773        0.042503\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:20     -455.322479       24.260961\n",
      "BFGS:    1 10:07:20     -462.708221       12.716908\n",
      "BFGS:    2 10:07:20     -420.183838      810.214593\n",
      "BFGS:    3 10:07:21     -448.525513      401.630194\n",
      "BFGS:    4 10:07:21     -462.614502      151.535114\n",
      "BFGS:    5 10:07:21     -466.401611       41.762389\n",
      "BFGS:    6 10:07:21     -467.296387       14.270499\n",
      "BFGS:    7 10:07:22     -467.473389        6.948873\n",
      "BFGS:    8 10:07:22     -467.834198        9.093753\n",
      "BFGS:    9 10:07:22     -468.163086        6.937519\n",
      "BFGS:   10 10:07:23     -468.330048        2.635352\n",
      "BFGS:   11 10:07:23     -468.432404        1.792055\n",
      "BFGS:   12 10:07:24     -468.503021        4.571756\n",
      "BFGS:   13 10:07:24     -468.580322        3.910026\n",
      "BFGS:   14 10:07:24     -468.601227        2.672631\n",
      "BFGS:   15 10:07:24     -468.648590        2.083844\n",
      "BFGS:   16 10:07:24     -468.667114        2.293042\n",
      "BFGS:   17 10:07:24     -468.683929        2.907281\n",
      "BFGS:   18 10:07:25     -468.701385        3.496423\n",
      "BFGS:   19 10:07:25     -468.716553        3.458974\n",
      "BFGS:   20 10:07:25     -468.727692        3.435082\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:25 - mcmc.system | INFO: Optimized structure has surface energy = 10.643\n",
      "10:07:25 - mcmc.mcmc | INFO: In sweep 2 out of 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:25     -446.097412       67.091261\n",
      "BFGS:    1 10:07:26     -462.391632       26.886955\n",
      "BFGS:    2 10:07:26     -468.394775       17.070749\n",
      "BFGS:    3 10:07:26     -472.648407       10.057305\n",
      "BFGS:    4 10:07:26     -475.249420        6.158516\n",
      "BFGS:    5 10:07:27     -476.881927        4.294027\n",
      "BFGS:    6 10:07:27     -477.939056        2.597173\n",
      "BFGS:    7 10:07:27     -478.381348        3.006327\n",
      "BFGS:    8 10:07:28     -478.828583        3.421656\n",
      "BFGS:    9 10:07:28     -479.445038        3.712381\n",
      "BFGS:   10 10:07:28     -480.029755        3.014633\n",
      "BFGS:   11 10:07:28     -480.507202        2.394754\n",
      "BFGS:   12 10:07:28     -480.953735        2.596301\n",
      "BFGS:   13 10:07:29     -481.332550        2.741582\n",
      "BFGS:   14 10:07:29     -481.862305        2.489037\n",
      "BFGS:   15 10:07:29     -482.250854        2.113957\n",
      "BFGS:   16 10:07:29     -482.503998        1.974578\n",
      "BFGS:   17 10:07:29     -482.780426        1.681017\n",
      "BFGS:   18 10:07:30     -483.008148        1.478091\n",
      "BFGS:   19 10:07:30     -483.250000        1.172348\n",
      "BFGS:   20 10:07:30     -483.371582        1.089836\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:30     -434.912964      117.245436\n",
      "BFGS:    1 10:07:30     -456.808441       39.673069\n",
      "BFGS:    2 10:07:31     -468.229584       31.259198\n",
      "BFGS:    3 10:07:31     -475.700104       14.637283\n",
      "BFGS:    4 10:07:31     -479.618408        7.978088\n",
      "BFGS:    5 10:07:31     -482.136322        4.420903\n",
      "BFGS:    6 10:07:31     -483.696381        2.613458\n",
      "BFGS:    7 10:07:31     -484.462189        2.413381\n",
      "BFGS:    8 10:07:32     -484.711334        2.504308\n",
      "BFGS:    9 10:07:32     -485.461395        3.302561\n",
      "BFGS:   10 10:07:32     -485.973145        3.613322\n",
      "BFGS:   11 10:07:32     -486.312988        2.032814\n",
      "BFGS:   12 10:07:33     -486.537964        1.852806\n",
      "BFGS:   13 10:07:33     -486.830170        1.367068\n",
      "BFGS:   14 10:07:33     -487.199371        1.218521\n",
      "BFGS:   15 10:07:33     -487.330078        1.125123\n",
      "BFGS:   16 10:07:33     -487.521484        1.214228\n",
      "BFGS:   17 10:07:34     -487.626312        1.159328\n",
      "BFGS:   18 10:07:34     -487.804108        1.024891\n",
      "BFGS:   19 10:07:34     -487.998047        1.166015\n",
      "BFGS:   20 10:07:34     -488.069977        2.057728\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:35     -448.560699       65.980233\n",
      "BFGS:    1 10:07:35     -465.456787       26.697754\n",
      "BFGS:    2 10:07:36     -472.185944       17.037054\n",
      "BFGS:    3 10:07:36     -476.647125       10.075923\n",
      "BFGS:    4 10:07:36     -479.492523        6.071920\n",
      "BFGS:    5 10:07:36     -481.247803        3.948298\n",
      "BFGS:    6 10:07:36     -482.327759        2.692293\n",
      "BFGS:    7 10:07:36     -482.808960        3.088832\n",
      "BFGS:    8 10:07:37     -483.223389        3.390364\n",
      "BFGS:    9 10:07:37     -483.939117        3.398821\n",
      "BFGS:   10 10:07:37     -484.527130        2.928737\n",
      "BFGS:   11 10:07:37     -484.978607        2.359620\n",
      "BFGS:   12 10:07:37     -485.413086        2.723141\n",
      "BFGS:   13 10:07:37     -485.819092        2.933130\n",
      "BFGS:   14 10:07:37     -486.282837        2.833297\n",
      "BFGS:   15 10:07:37     -486.620819        2.506992\n",
      "BFGS:   16 10:07:38     -487.022125        1.105146\n",
      "BFGS:   17 10:07:38     -487.132812        0.994551\n",
      "BFGS:   18 10:07:38     -487.324005        1.061160\n",
      "BFGS:   19 10:07:38     -487.406036        0.914776\n",
      "BFGS:   20 10:07:38     -487.470062        0.719552\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:38     -452.069489       66.158726\n",
      "BFGS:    1 10:07:39     -469.005035       26.876889\n",
      "BFGS:    2 10:07:39     -476.483276       17.006984\n",
      "BFGS:    3 10:07:39     -482.105377        9.884987\n",
      "BFGS:    4 10:07:39     -485.946136        6.027884\n",
      "BFGS:    5 10:07:40     -488.330231        4.050878\n",
      "BFGS:    6 10:07:40     -489.599945        2.712798\n",
      "BFGS:    7 10:07:40     -490.271149        2.752068\n",
      "BFGS:    8 10:07:41     -490.815338        3.436377\n",
      "BFGS:    9 10:07:41     -491.565063        3.723107\n",
      "BFGS:   10 10:07:41     -492.249481        3.294494\n",
      "BFGS:   11 10:07:41     -492.762665        2.744366\n",
      "BFGS:   12 10:07:41     -493.272614        2.390779\n",
      "BFGS:   13 10:07:41     -493.791504        2.333514\n",
      "BFGS:   14 10:07:42     -494.478912        2.334536\n",
      "BFGS:   15 10:07:42     -495.055542        3.123156\n",
      "BFGS:   16 10:07:42     -495.586945        3.682281\n",
      "BFGS:   17 10:07:42     -496.173981        5.366265\n",
      "BFGS:   18 10:07:42     -496.970795        3.915348\n",
      "BFGS:   19 10:07:42     -498.300171        1.949075\n",
      "BFGS:   20 10:07:42     -498.611542        2.908960\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:43     -462.749847       43.124643\n",
      "BFGS:    1 10:07:43     -473.607452       19.840179\n",
      "BFGS:    2 10:07:43     -478.664948       11.278721\n",
      "BFGS:    3 10:07:43     -481.994232        6.201207\n",
      "BFGS:    4 10:07:43     -484.396576        5.266307\n",
      "BFGS:    5 10:07:44     -486.136200        3.911769\n",
      "BFGS:    6 10:07:44     -487.148682        3.071416\n",
      "BFGS:    7 10:07:44     -487.568268        4.017641\n",
      "BFGS:    8 10:07:44     -488.032593        2.776328\n",
      "BFGS:    9 10:07:44     -488.744720        1.803730\n",
      "BFGS:   10 10:07:44     -489.089844        1.880893\n",
      "BFGS:   11 10:07:45     -489.386627        2.799618\n",
      "BFGS:   12 10:07:45     -489.973389        3.573342\n",
      "BFGS:   13 10:07:45     -490.534668        3.433735\n",
      "BFGS:   14 10:07:45     -490.992676        2.732190\n",
      "BFGS:   15 10:07:45     -491.382568        2.534108\n",
      "BFGS:   16 10:07:45     -491.697357        1.944246\n",
      "BFGS:   17 10:07:45     -492.170380        2.106657\n",
      "BFGS:   18 10:07:45     -492.576874        1.684177\n",
      "BFGS:   19 10:07:46     -492.796173        1.202915\n",
      "BFGS:   20 10:07:46     -493.006927        1.142715\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:46 - mcmc.system | INFO: Optimized structure has surface energy = 9.490\n",
      "10:07:46 - mcmc.mcmc | INFO: In sweep 3 out of 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:46     -446.857819       64.835711\n",
      "BFGS:    1 10:07:46     -463.641235       24.956379\n",
      "BFGS:    2 10:07:46     -470.958221       14.532101\n",
      "BFGS:    3 10:07:46     -476.034027        8.156289\n",
      "BFGS:    4 10:07:47     -479.151764        4.207033\n",
      "BFGS:    5 10:07:47     -480.936859        2.236077\n",
      "BFGS:    6 10:07:47     -481.841187        1.928059\n",
      "BFGS:    7 10:07:47     -482.182495        1.797030\n",
      "BFGS:    8 10:07:47     -482.806488        3.536799\n",
      "BFGS:    9 10:07:47     -483.248260        2.413343\n",
      "BFGS:   10 10:07:47     -483.733734        1.261910\n",
      "BFGS:   11 10:07:48     -483.854584        1.302530\n",
      "BFGS:   12 10:07:48     -484.093658        1.526466\n",
      "BFGS:   13 10:07:48     -484.372955        1.803537\n",
      "BFGS:   14 10:07:48     -484.679901        1.807443\n",
      "BFGS:   15 10:07:48     -485.072998        2.149097\n",
      "BFGS:   16 10:07:48     -485.426361        2.478797\n",
      "BFGS:   17 10:07:49     -485.721588        2.372428\n",
      "BFGS:   18 10:07:49     -486.136719        1.535215\n",
      "BFGS:   19 10:07:49     -486.392914        1.366560\n",
      "BFGS:   20 10:07:49     -486.596039        1.492182\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:50     -451.708771       67.034400\n",
      "BFGS:    1 10:07:50     -469.015747       28.151236\n",
      "BFGS:    2 10:07:50     -475.961334       18.614063\n",
      "BFGS:    3 10:07:50     -480.882660       12.199688\n",
      "BFGS:    4 10:07:50     -484.252472        7.829387\n",
      "BFGS:    5 10:07:50     -486.528412        5.720668\n",
      "BFGS:    6 10:07:51     -488.116577        4.549375\n",
      "BFGS:    7 10:07:51     -489.074860        3.221978\n",
      "BFGS:    8 10:07:51     -489.770966        3.261236\n",
      "BFGS:    9 10:07:51     -490.319946        3.860254\n",
      "BFGS:   10 10:07:51     -491.017914        3.821377\n",
      "BFGS:   11 10:07:52     -491.659302        3.388476\n",
      "BFGS:   12 10:07:52     -492.363861        2.786741\n",
      "BFGS:   13 10:07:52     -492.904816        2.721747\n",
      "BFGS:   14 10:07:52     -493.576080        2.422094\n",
      "BFGS:   15 10:07:52     -494.194672        1.450537\n",
      "BFGS:   16 10:07:53     -494.499359        1.188426\n",
      "BFGS:   17 10:07:53     -494.659271        1.130465\n",
      "BFGS:   18 10:07:53     -494.888153        0.970207\n",
      "BFGS:   19 10:07:53     -494.977295        0.727638\n",
      "BFGS:   20 10:07:53     -495.025879        0.692567\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:53     -415.725128       98.368026\n",
      "BFGS:    1 10:07:53     -194.873352     4221.945760\n",
      "BFGS:    2 10:07:53     -315.704620     2296.277313\n",
      "BFGS:    3 10:07:54     -391.935089     1202.031146\n",
      "BFGS:    4 10:07:54     -427.366577      654.452156\n",
      "BFGS:    5 10:07:54     -454.534943      259.704038\n",
      "BFGS:    6 10:07:54     -468.986938      144.929926\n",
      "BFGS:    7 10:07:54     -453.038727      453.736777\n",
      "BFGS:    8 10:07:54     -474.435516       30.197558\n",
      "BFGS:    9 10:07:54     -475.978973       41.184269\n",
      "BFGS:   10 10:07:55     -478.796387       36.496099\n",
      "BFGS:   11 10:07:55     -479.087982       45.565988\n",
      "BFGS:   12 10:07:55     -482.169464       61.729841\n",
      "BFGS:   13 10:07:55     -485.016754       59.964316\n",
      "BFGS:   14 10:07:55     -488.105133      130.742606\n",
      "BFGS:   15 10:07:56     -488.992340      125.330103\n",
      "BFGS:   16 10:07:56     -491.039093       23.881372\n",
      "BFGS:   17 10:07:56     -491.328735      101.141261\n",
      "BFGS:   18 10:07:56     -491.045074      134.260293\n",
      "BFGS:   19 10:07:56     -493.527466      154.364511\n",
      "BFGS:   20 10:07:57     -493.743317      137.214967\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:07:57     -423.686920      446.737846\n",
      "BFGS:    1 10:07:57     -433.156281       96.666550\n",
      "BFGS:    2 10:07:57     -453.722046       44.341765\n",
      "BFGS:    3 10:07:57     -465.433441       47.734298\n",
      "BFGS:    4 10:07:57     -474.583862       29.009183\n",
      "BFGS:    5 10:07:58     -480.908203       18.630383\n",
      "BFGS:    6 10:07:58     -485.052612       10.739525\n",
      "BFGS:    7 10:07:58     -487.661102        5.774450\n",
      "BFGS:    8 10:07:58     -489.178955        3.807754\n",
      "BFGS:    9 10:07:58     -489.981354        3.285964\n",
      "BFGS:   10 10:07:58     -490.644409        3.210281\n",
      "BFGS:   11 10:07:59     -491.025024        3.067775\n",
      "BFGS:   12 10:07:59     -491.378784        7.356585\n",
      "BFGS:   13 10:07:59     -491.951752        2.013839\n",
      "BFGS:   14 10:08:00     -492.184174        1.921303\n",
      "BFGS:   15 10:08:00     -492.666718        2.429611\n",
      "BFGS:   16 10:08:00     -492.839722        2.100568\n",
      "BFGS:   17 10:08:00     -493.138275        1.587077\n",
      "BFGS:   18 10:08:00     -493.326630        1.397056\n",
      "BFGS:   19 10:08:01     -493.507172        1.461272\n",
      "BFGS:   20 10:08:01     -493.682648        1.388795\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:01     -455.368988       67.445906\n",
      "BFGS:    1 10:08:01     -472.932953       28.313437\n",
      "BFGS:    2 10:08:01     -480.422394       18.708341\n",
      "BFGS:    3 10:08:01     -486.087402       11.657002\n",
      "BFGS:    4 10:08:02     -489.826416        7.030529\n",
      "BFGS:    5 10:08:02     -492.116058        4.807762\n",
      "BFGS:    6 10:08:02     -493.640045        3.284434\n",
      "BFGS:    7 10:08:02     -494.472046        2.338222\n",
      "BFGS:    8 10:08:02     -494.917328        2.396563\n",
      "BFGS:    9 10:08:02     -495.390472        2.762038\n",
      "BFGS:   10 10:08:02     -495.867157        2.750356\n",
      "BFGS:   11 10:08:03     -496.500031        2.022076\n",
      "BFGS:   12 10:08:03     -496.775604        2.072439\n",
      "BFGS:   13 10:08:03     -497.312775        2.267596\n",
      "BFGS:   14 10:08:03     -497.665436        2.693729\n",
      "BFGS:   15 10:08:03     -498.054840        2.153062\n",
      "BFGS:   16 10:08:03     -498.477997        1.860271\n",
      "BFGS:   17 10:08:03     -498.927338        1.650628\n",
      "BFGS:   18 10:08:03     -499.150879        1.396168\n",
      "BFGS:   19 10:08:04     -499.233917        1.099690\n",
      "BFGS:   20 10:08:04     -499.373932        0.783668\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:08:04 - mcmc.system | INFO: Optimized structure has surface energy = 7.244\n",
      "10:08:04 - mcmc.mcmc | INFO: In sweep 4 out of 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:04     -453.464996       67.578112\n",
      "BFGS:    1 10:08:04     -472.656342       28.319092\n",
      "BFGS:    2 10:08:04     -481.944702       18.110983\n",
      "BFGS:    3 10:08:05     -488.852539       11.921911\n",
      "BFGS:    4 10:08:05     -493.212799        7.059559\n",
      "BFGS:    5 10:08:05     -495.572174        4.794649\n",
      "BFGS:    6 10:08:05     -497.145721        3.146207\n",
      "BFGS:    7 10:08:05     -498.102936        2.423294\n",
      "BFGS:    8 10:08:06     -498.815826        2.518794\n",
      "BFGS:    9 10:08:06     -499.384033        3.070525\n",
      "BFGS:   10 10:08:06     -500.299530        2.895494\n",
      "BFGS:   11 10:08:06     -500.645294        1.766248\n",
      "BFGS:   12 10:08:06     -501.152191        1.690305\n",
      "BFGS:   13 10:08:06     -501.560669        2.219827\n",
      "BFGS:   14 10:08:06     -501.896149        2.392355\n",
      "BFGS:   15 10:08:06     -502.410065        2.136756\n",
      "BFGS:   16 10:08:06     -502.796539        2.042862\n",
      "BFGS:   17 10:08:06     -503.223175        1.515847\n",
      "BFGS:   18 10:08:06     -503.473633        1.553414\n",
      "BFGS:   19 10:08:07     -503.694061        1.118267\n",
      "BFGS:   20 10:08:07     -503.856171        0.847189\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:08     -389.345551      171.391086\n",
      "BFGS:    1 10:08:08     -443.202728       74.794951\n",
      "BFGS:    2 10:08:08     -463.035797       53.120919\n",
      "BFGS:    3 10:08:09     -477.756714       28.638129\n",
      "BFGS:    4 10:08:09     -487.406097       17.533918\n",
      "BFGS:    5 10:08:09     -492.597565       10.779894\n",
      "BFGS:    6 10:08:09     -496.085571        6.689719\n",
      "BFGS:    7 10:08:09     -498.096680        4.852998\n",
      "BFGS:    8 10:08:09     -499.316040        3.430927\n",
      "BFGS:    9 10:08:10     -500.469818        3.269347\n",
      "BFGS:   10 10:08:10     -501.680176        3.578196\n",
      "BFGS:   11 10:08:10     -502.792999        3.215484\n",
      "BFGS:   12 10:08:10     -503.560211        2.012205\n",
      "BFGS:   13 10:08:10     -504.009521        2.255283\n",
      "BFGS:   14 10:08:10     -504.588623        2.224876\n",
      "BFGS:   15 10:08:10     -505.075836        2.154315\n",
      "BFGS:   16 10:08:11     -505.464355        2.302987\n",
      "BFGS:   17 10:08:11     -505.864105        2.202562\n",
      "BFGS:   18 10:08:11     -506.328949        1.858116\n",
      "BFGS:   19 10:08:11     -506.628632        1.620596\n",
      "BFGS:   20 10:08:11     -506.834747        1.328171\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:11     -380.226959      182.108002\n",
      "BFGS:    1 10:08:11     -426.830048      190.041895\n",
      "BFGS:    2 10:08:12     -430.553375      200.693991\n",
      "BFGS:    3 10:08:12     -449.334076      206.628875\n",
      "BFGS:    4 10:08:12     -468.320404       94.069316\n",
      "BFGS:    5 10:08:12     -477.807220       20.718941\n",
      "BFGS:    6 10:08:12     -479.427856      118.316062\n",
      "BFGS:    7 10:08:12     -484.850128       38.021703\n",
      "BFGS:    8 10:08:12     -487.513794       10.489028\n",
      "BFGS:    9 10:08:12     -488.828613        5.935719\n",
      "BFGS:   10 10:08:13     -490.914154       22.013094\n",
      "BFGS:   11 10:08:13     -492.091156        4.579460\n",
      "BFGS:   12 10:08:13     -493.112274        5.812498\n",
      "BFGS:   13 10:08:13     -494.586304       12.230098\n",
      "BFGS:   14 10:08:13     -495.075165       36.564703\n",
      "BFGS:   15 10:08:13     -495.499756       32.008052\n",
      "BFGS:   16 10:08:13     -495.976044       11.879427\n",
      "BFGS:   17 10:08:14     -496.402618        3.991574\n",
      "BFGS:   18 10:08:14     -496.858978       11.303050\n",
      "BFGS:   19 10:08:14     -497.156982        2.934413\n",
      "BFGS:   20 10:08:15     -497.442780        3.223151\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:15     -354.172028      170.298382\n",
      "BFGS:    1 10:08:15     -417.951904       76.296256\n",
      "BFGS:    2 10:08:16     -445.116791       61.417049\n",
      "BFGS:    3 10:08:16     -462.570465       36.515896\n",
      "BFGS:    4 10:08:16     -477.403809       25.835992\n",
      "BFGS:    5 10:08:16     -486.232574       17.117481\n",
      "BFGS:    6 10:08:16     -492.479584       11.537145\n",
      "BFGS:    7 10:08:16     -496.060394        7.350080\n",
      "BFGS:    8 10:08:16     -498.025055        5.077090\n",
      "BFGS:    9 10:08:17     -499.445282        4.306061\n",
      "BFGS:   10 10:08:17     -500.621185        3.402868\n",
      "BFGS:   11 10:08:17     -501.988281        2.880742\n",
      "BFGS:   12 10:08:17     -503.316071        2.409368\n",
      "BFGS:   13 10:08:17     -503.934326        2.633004\n",
      "BFGS:   14 10:08:17     -504.649323        2.644020\n",
      "BFGS:   15 10:08:18     -505.448334        2.532464\n",
      "BFGS:   16 10:08:18     -506.007904        2.729947\n",
      "BFGS:   17 10:08:18     -506.571808        2.318776\n",
      "BFGS:   18 10:08:18     -507.085907        1.790826\n",
      "BFGS:   19 10:08:18     -507.750275        2.410830\n",
      "BFGS:   20 10:08:18     -508.086578        1.568602\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:18     -364.518921      159.428239\n",
      "BFGS:    1 10:08:18     -426.381866       88.649734\n",
      "BFGS:    2 10:08:18     -169.570251     4793.526885\n",
      "BFGS:    3 10:08:18     -433.473053      595.709897\n",
      "BFGS:    4 10:08:19     -465.066162       54.382587\n",
      "BFGS:    5 10:08:19     -476.065277       53.171274\n",
      "BFGS:    6 10:08:19     -484.691010       19.695539\n",
      "BFGS:    7 10:08:19     -487.982697        6.226478\n",
      "BFGS:    8 10:08:19     -490.593109       14.402026\n",
      "BFGS:    9 10:08:19     -492.187592       18.280419\n",
      "BFGS:   10 10:08:19     -493.015533       41.057284\n",
      "BFGS:   11 10:08:19     -494.298828        6.983640\n",
      "BFGS:   12 10:08:19     -495.190948       11.303818\n",
      "BFGS:   13 10:08:20     -496.101654       12.185693\n",
      "BFGS:   14 10:08:20     -496.915924       11.962373\n",
      "BFGS:   15 10:08:20     -497.488190        2.559619\n",
      "BFGS:   16 10:08:20     -498.141022        4.916554\n",
      "BFGS:   17 10:08:20     -498.432861        6.841445\n",
      "BFGS:   18 10:08:20     -498.859375        2.521724\n",
      "BFGS:   19 10:08:21     -499.182465        3.226701\n",
      "BFGS:   20 10:08:21     -499.486481        6.326031\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:08:21 - mcmc.system | INFO: Optimized structure has surface energy = 8.257\n",
      "10:08:21 - mcmc.mcmc | INFO: In sweep 5 out of 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:21     -357.041412      165.907595\n",
      "BFGS:    1 10:08:21     -420.410370      107.306416\n",
      "BFGS:    2 10:08:21     -449.186371       64.300176\n",
      "BFGS:    3 10:08:21     -465.727631       42.305498\n",
      "BFGS:    4 10:08:22     -481.913849       26.419327\n",
      "BFGS:    5 10:08:22     -492.524139       15.894928\n",
      "BFGS:    6 10:08:22     -498.516510        8.501376\n",
      "BFGS:    7 10:08:22     -502.083038        4.308381\n",
      "BFGS:    8 10:08:22     -503.256439        8.504122\n",
      "BFGS:    9 10:08:22     -504.460602        2.363761\n",
      "BFGS:   10 10:08:22     -505.564941        2.494769\n",
      "BFGS:   11 10:08:23     -506.279022        3.139295\n",
      "BFGS:   12 10:08:23     -507.024658        3.308939\n",
      "BFGS:   13 10:08:23     -507.998627        2.944256\n",
      "BFGS:   14 10:08:23     -508.727448        2.987732\n",
      "BFGS:   15 10:08:23     -509.188477        2.472855\n",
      "BFGS:   16 10:08:23     -509.968750        3.343061\n",
      "BFGS:   17 10:08:23     -510.271393        2.202648\n",
      "BFGS:   18 10:08:23     -511.038177        2.273752\n",
      "BFGS:   19 10:08:23     -511.300934        2.242049\n",
      "BFGS:   20 10:08:24     -511.710358        1.760277\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:24     -385.411285      171.137229\n",
      "BFGS:    1 10:08:24     -440.417633       74.914408\n",
      "BFGS:    2 10:08:24     -462.586578       54.357787\n",
      "BFGS:    3 10:08:24     -477.610199       32.413094\n",
      "BFGS:    4 10:08:24     -486.720215       24.323606\n",
      "BFGS:    5 10:08:24     -494.273041       15.104843\n",
      "BFGS:    6 10:08:24     -498.840576       10.072490\n",
      "BFGS:    7 10:08:24     -501.653351        5.902200\n",
      "BFGS:    8 10:08:25     -503.215576        4.255443\n",
      "BFGS:    9 10:08:25     -504.142426        3.079241\n",
      "BFGS:   10 10:08:25     -505.639374        4.324400\n",
      "BFGS:   11 10:08:25     -507.050781        4.489665\n",
      "BFGS:   12 10:08:25     -508.129517        2.997737\n",
      "BFGS:   13 10:08:25     -509.009979        2.295056\n",
      "BFGS:   14 10:08:25     -509.720245        2.418467\n",
      "BFGS:   15 10:08:26     -510.136932        2.347665\n",
      "BFGS:   16 10:08:26     -510.593842        1.730764\n",
      "BFGS:   17 10:08:26     -510.915314        1.372446\n",
      "BFGS:   18 10:08:26     -511.124664        1.337705\n",
      "BFGS:   19 10:08:26     -511.318481        1.152535\n",
      "BFGS:   20 10:08:26     -511.498291        1.084224\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:26     -330.902069      455.496896\n",
      "BFGS:    1 10:08:26     -365.107147      142.344474\n",
      "BFGS:    2 10:08:26     -401.338715      328.185030\n",
      "BFGS:    3 10:08:27     -428.809296       51.506117\n",
      "BFGS:    4 10:08:27     -447.779175       64.892651\n",
      "BFGS:    5 10:08:27     -461.142242       65.757359\n",
      "BFGS:    6 10:08:27     -471.861816       40.259778\n",
      "BFGS:    7 10:08:27     -480.194092       13.860091\n",
      "BFGS:    8 10:08:27     -483.653809       36.649161\n",
      "BFGS:    9 10:08:27     -486.295532       32.188190\n",
      "BFGS:   10 10:08:27     -488.221527        8.984876\n",
      "BFGS:   11 10:08:27     -489.386963       23.494698\n",
      "BFGS:   12 10:08:28     -490.548187        2.729718\n",
      "BFGS:   13 10:08:28     -491.957977        7.514346\n",
      "BFGS:   14 10:08:28     -492.983307        3.192400\n",
      "BFGS:   15 10:08:28     -493.520386        3.206283\n",
      "BFGS:   16 10:08:28     -494.420563        3.931984\n",
      "BFGS:   17 10:08:28     -494.719269        3.942424\n",
      "BFGS:   18 10:08:28     -495.086823        2.860081\n",
      "BFGS:   19 10:08:29     -495.451752        9.976706\n",
      "BFGS:   20 10:08:29     -495.708588        4.024747\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:29     -358.698944      158.821699\n",
      "BFGS:    1 10:08:29     -421.463989       66.703903\n",
      "BFGS:    2 10:08:29     -443.439209       63.146061\n",
      "BFGS:    3 10:08:29     -463.169464       33.043423\n",
      "BFGS:    4 10:08:29     -476.527008       22.218739\n",
      "BFGS:    5 10:08:29     -485.031830       14.075428\n",
      "BFGS:    6 10:08:29     -490.252594        9.700834\n",
      "BFGS:    7 10:08:30     -493.847076        5.961746\n",
      "BFGS:    8 10:08:30     -496.307770        4.067973\n",
      "BFGS:    9 10:08:30     -497.847656        3.589422\n",
      "BFGS:   10 10:08:30     -499.109131        3.225879\n",
      "BFGS:   11 10:08:30     -500.247314        3.713784\n",
      "BFGS:   12 10:08:30     -501.591400        2.929533\n",
      "BFGS:   13 10:08:30     -502.583527        2.812777\n",
      "BFGS:   14 10:08:30     -503.210175        2.536806\n",
      "BFGS:   15 10:08:30     -503.881836        2.599626\n",
      "BFGS:   16 10:08:30     -504.764648        3.678421\n",
      "BFGS:   17 10:08:31     -505.734467        3.814335\n",
      "BFGS:   18 10:08:31     -506.317596        2.709941\n",
      "BFGS:   19 10:08:31     -506.704498        2.017948\n",
      "BFGS:   20 10:08:31     -507.146851        1.911864\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:31    34371.082031   814895.691975\n",
      "BFGS:    1 10:08:31     -370.068878      351.407494\n",
      "BFGS:    2 10:08:31     -424.077271      357.307783\n",
      "BFGS:    3 10:08:31     -451.146362      283.328307\n",
      "BFGS:    4 10:08:31     -473.542114       53.510981\n",
      "BFGS:    5 10:08:32     -486.616364       20.237293\n",
      "BFGS:    6 10:08:32     -493.417755       11.561173\n",
      "BFGS:    7 10:08:32     -497.358551        6.713994\n",
      "BFGS:    8 10:08:32     -499.656403        4.239102\n",
      "BFGS:    9 10:08:32     -500.898285        4.552820\n",
      "BFGS:   10 10:08:32     -501.917603        4.748853\n",
      "BFGS:   11 10:08:32     -502.763824        4.546518\n",
      "BFGS:   12 10:08:33     -503.838867        3.616818\n",
      "BFGS:   13 10:08:33     -504.060425        3.025722\n",
      "BFGS:   14 10:08:33     -504.510590        1.961910\n",
      "BFGS:   15 10:08:33     -504.807709        2.105327\n",
      "BFGS:   16 10:08:33     -505.172119        2.590776\n",
      "BFGS:   17 10:08:33     -505.445312        3.026568\n",
      "BFGS:   18 10:08:33     -505.975098        3.085223\n",
      "BFGS:   19 10:08:34     -506.168427        2.128645\n",
      "BFGS:   20 10:08:34     -506.473633        1.752974\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:08:34 - mcmc.system | INFO: Optimized structure has surface energy = 8.257\n",
      "10:08:34 - mcmc.mcmc | INFO: In sweep 6 out of 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:34     -393.107635      171.584641\n",
      "BFGS:    1 10:08:34     -447.747559       75.918214\n",
      "BFGS:    2 10:08:34     -469.214935       53.394166\n",
      "BFGS:    3 10:08:34     -484.437042       29.576447\n",
      "BFGS:    4 10:08:34     -494.505524       18.957975\n",
      "BFGS:    5 10:08:34     -499.981964       12.093645\n",
      "BFGS:    6 10:08:34     -503.604248        7.585843\n",
      "BFGS:    7 10:08:35     -505.831573        5.441567\n",
      "BFGS:    8 10:08:35     -507.090912        4.087731\n",
      "BFGS:    9 10:08:35     -508.059052        2.871097\n",
      "BFGS:   10 10:08:35     -508.911377        2.679605\n",
      "BFGS:   11 10:08:35     -510.339691        2.458251\n",
      "BFGS:   12 10:08:35     -511.354950        2.290993\n",
      "BFGS:   13 10:08:35     -511.984650        2.689228\n",
      "BFGS:   14 10:08:36     -512.619080        2.369054\n",
      "BFGS:   15 10:08:36     -513.529785        2.684913\n",
      "BFGS:   16 10:08:36     -513.915710        2.541573\n",
      "BFGS:   17 10:08:36     -514.531311        1.951378\n",
      "BFGS:   18 10:08:36     -514.879639        1.497189\n",
      "BFGS:   19 10:08:36     -515.167542        1.222171\n",
      "BFGS:   20 10:08:36     -515.332092        1.181032\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:37     -384.694122      180.958100\n",
      "BFGS:    1 10:08:37     -433.636993      134.296271\n",
      "BFGS:    2 10:08:37     -425.538330       90.242701\n",
      "BFGS:    3 10:08:37     -444.009888       94.547457\n",
      "BFGS:    4 10:08:37     -464.759644      171.282598\n",
      "BFGS:    5 10:08:37     -480.432343      149.478508\n",
      "BFGS:    6 10:08:37     -485.159027       23.270535\n",
      "BFGS:    7 10:08:37     -487.714996       13.996501\n",
      "BFGS:    8 10:08:37     -490.874634       88.547003\n",
      "BFGS:    9 10:08:38     -493.263397       12.623983\n",
      "BFGS:   10 10:08:38     -496.370117       11.760767\n",
      "BFGS:   11 10:08:38     -497.763214        8.658680\n",
      "BFGS:   12 10:08:38     -498.732788        6.733482\n",
      "BFGS:   13 10:08:38     -499.514648       15.460809\n",
      "BFGS:   14 10:08:38     -501.281006       25.229460\n",
      "BFGS:   15 10:08:38     -502.882812       33.537302\n",
      "BFGS:   16 10:08:38     -504.021149       31.328855\n",
      "BFGS:   17 10:08:39     -504.917084       11.368698\n",
      "BFGS:   18 10:08:39     -505.929565       12.721445\n",
      "BFGS:   19 10:08:39     -507.284088       29.382229\n",
      "BFGS:   20 10:08:39     -505.755463       82.656256\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:39     -384.812653      171.751871\n",
      "BFGS:    1 10:08:39     -439.659576       75.694744\n",
      "BFGS:    2 10:08:39     -399.366974     1040.274663\n",
      "BFGS:    3 10:08:40     -458.187286      351.766302\n",
      "BFGS:    4 10:08:40     -480.825653       34.825719\n",
      "BFGS:    5 10:08:40     -490.236725       18.385245\n",
      "BFGS:    6 10:08:40     -496.730377       12.160946\n",
      "BFGS:    7 10:08:40     -500.628998       12.430602\n",
      "BFGS:    8 10:08:40     -503.137451        5.919924\n",
      "BFGS:    9 10:08:40     -504.725616        8.201211\n",
      "BFGS:   10 10:08:41     -505.945679        7.041177\n",
      "BFGS:   11 10:08:41     -507.261505        4.114158\n",
      "BFGS:   12 10:08:41     -509.015228       10.732312\n",
      "BFGS:   13 10:08:41     -510.041168        9.669148\n",
      "BFGS:   14 10:08:41     -510.587402        4.852108\n",
      "BFGS:   15 10:08:41     -511.435944        5.441717\n",
      "BFGS:   16 10:08:42     -511.913116        7.181066\n",
      "BFGS:   17 10:08:42     -512.366516        4.146460\n",
      "BFGS:   18 10:08:42     -512.750488        1.494149\n",
      "BFGS:   19 10:08:42     -513.071655        6.675159\n",
      "BFGS:   20 10:08:42     -513.288025        3.304699\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:42     -350.340576      136.336383\n",
      "BFGS:    1 10:08:42     -418.241241      111.215211\n",
      "BFGS:    2 10:08:43     -399.259033     1082.488279\n",
      "BFGS:    3 10:08:43     -442.492584      571.229267\n",
      "BFGS:    4 10:08:43     -472.498749      125.612624\n",
      "BFGS:    5 10:08:43     -479.530396      173.771166\n",
      "BFGS:    6 10:08:43     -490.856171       31.792492\n",
      "BFGS:    7 10:08:43     -495.674408       15.597235\n",
      "BFGS:    8 10:08:43     -497.688568        8.967768\n",
      "BFGS:    9 10:08:43     -499.696198       42.676299\n",
      "BFGS:   10 10:08:43     -501.238983       25.091211\n",
      "BFGS:   11 10:08:44     -502.795166        6.370777\n",
      "BFGS:   12 10:08:44     -504.144501       22.418104\n",
      "BFGS:   13 10:08:44     -505.391998       19.096563\n",
      "BFGS:   14 10:08:44     -506.337158        4.610591\n",
      "BFGS:   15 10:08:44     -507.622894        7.755071\n",
      "BFGS:   16 10:08:44     -508.762543       34.573931\n",
      "BFGS:   17 10:08:44     -508.955658       23.535442\n",
      "BFGS:   18 10:08:44     -510.113617       11.170660\n",
      "BFGS:   19 10:08:44     -510.698761       11.187237\n",
      "BFGS:   20 10:08:45     -511.186188        3.151538\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:45     -358.821930      507.865017\n",
      "BFGS:    1 10:08:45     -411.892731      147.734546\n",
      "BFGS:    2 10:08:45     -451.919037       56.986153\n",
      "BFGS:    3 10:08:45     -473.952606       47.296372\n",
      "BFGS:    4 10:08:45     -490.076508       38.721702\n",
      "BFGS:    5 10:08:45     -501.479218       26.521096\n",
      "BFGS:    6 10:08:46     -508.416046       17.114542\n",
      "BFGS:    7 10:08:46     -513.348083        8.743938\n",
      "BFGS:    8 10:08:46     -516.601990        5.468574\n",
      "BFGS:    9 10:08:46     -517.653259        4.472584\n",
      "BFGS:   10 10:08:46     -518.916260        3.783650\n",
      "BFGS:   11 10:08:46     -520.195007        3.689089\n",
      "BFGS:   12 10:08:46     -521.025818        3.136647\n",
      "BFGS:   13 10:08:46     -522.279846        2.932585\n",
      "BFGS:   14 10:08:47     -523.047424        3.052425\n",
      "BFGS:   15 10:08:47     -523.637146        3.000512\n",
      "BFGS:   16 10:08:47     -524.520325        2.680269\n",
      "BFGS:   17 10:08:47     -525.128052        2.731311\n",
      "BFGS:   18 10:08:47     -525.587097        1.853204\n",
      "BFGS:   19 10:08:47     -525.977722        1.793734\n",
      "BFGS:   20 10:08:47     -526.371887        1.695771\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:08:48 - mcmc.system | INFO: Optimized structure has surface energy = 4.589\n",
      "10:08:48 - mcmc.mcmc | INFO: In sweep 7 out of 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:48     -359.039398      189.135833\n",
      "BFGS:    1 10:08:48     -430.957245       79.034549\n",
      "BFGS:    2 10:08:48     -453.538239       76.672147\n",
      "BFGS:    3 10:08:48     -472.873932       51.424603\n",
      "BFGS:    4 10:08:49     -486.079132       33.698927\n",
      "BFGS:    5 10:08:49     -495.464600       22.785327\n",
      "BFGS:    6 10:08:49     -502.118011       15.139800\n",
      "BFGS:    7 10:08:49     -506.861237       10.489931\n",
      "BFGS:    8 10:08:49     -510.198608        7.315132\n",
      "BFGS:    9 10:08:49     -512.744507        6.241527\n",
      "BFGS:   10 10:08:49     -514.357361        5.400005\n",
      "BFGS:   11 10:08:49     -515.343811        5.076230\n",
      "BFGS:   12 10:08:49     -516.467102        4.064600\n",
      "BFGS:   13 10:08:49     -517.715759        3.437828\n",
      "BFGS:   14 10:08:50     -518.542480        3.397683\n",
      "BFGS:   15 10:08:50     -519.416077        3.352732\n",
      "BFGS:   16 10:08:50     -520.279358        2.741654\n",
      "BFGS:   17 10:08:50     -521.192566        2.967243\n",
      "BFGS:   18 10:08:50     -522.333435        2.253444\n",
      "BFGS:   19 10:08:50     -523.029053        2.828330\n",
      "BFGS:   20 10:08:50     -523.739563        3.660689\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:50     -378.864136      168.066922\n",
      "BFGS:    1 10:08:51     -433.108368       76.896164\n",
      "BFGS:    2 10:08:51     -459.475433       55.287936\n",
      "BFGS:    3 10:08:51     -477.996735       31.790179\n",
      "BFGS:    4 10:08:51     -489.312164       20.924251\n",
      "BFGS:    5 10:08:51     -495.902740       12.779438\n",
      "BFGS:    6 10:08:51     -500.136353        8.982242\n",
      "BFGS:    7 10:08:51     -502.914673        5.913714\n",
      "BFGS:    8 10:08:51     -505.011993        5.058203\n",
      "BFGS:    9 10:08:51     -506.758148        4.034350\n",
      "BFGS:   10 10:08:52     -508.029449        3.246633\n",
      "BFGS:   11 10:08:52     -509.110138        2.972283\n",
      "BFGS:   12 10:08:52     -510.083496        2.452536\n",
      "BFGS:   13 10:08:52     -511.195068        3.689622\n",
      "BFGS:   14 10:08:52     -511.927582        2.031491\n",
      "BFGS:   15 10:08:52     -512.324280        1.483510\n",
      "BFGS:   16 10:08:52     -512.571045        1.596294\n",
      "BFGS:   17 10:08:52     -512.903809        1.739478\n",
      "BFGS:   18 10:08:52     -513.130432        1.482973\n",
      "BFGS:   19 10:08:52     -513.403870        1.806348\n",
      "BFGS:   20 10:08:52     -513.690857        2.597813\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:53     -384.324799      167.944356\n",
      "BFGS:    1 10:08:53     -439.272125       79.512486\n",
      "BFGS:    2 10:08:53     -460.188019       62.178364\n",
      "BFGS:    3 10:08:53     -475.151947       42.537257\n",
      "BFGS:    4 10:08:53     -488.977325       24.405258\n",
      "BFGS:    5 10:08:53     -497.588898       14.621186\n",
      "BFGS:    6 10:08:54     -502.216461        7.708101\n",
      "BFGS:    7 10:08:54     -505.020386        4.844303\n",
      "BFGS:    8 10:08:54     -506.675629        3.781104\n",
      "BFGS:    9 10:08:54     -507.884888        3.962615\n",
      "BFGS:   10 10:08:54     -508.335815        5.288442\n",
      "BFGS:   11 10:08:54     -509.331940        2.340254\n",
      "BFGS:   12 10:08:54     -509.933380        2.663486\n",
      "BFGS:   13 10:08:54     -510.737671        2.607094\n",
      "BFGS:   14 10:08:54     -511.122437        1.967289\n",
      "BFGS:   15 10:08:54     -511.536285        1.978486\n",
      "BFGS:   16 10:08:55     -512.096008        2.252129\n",
      "BFGS:   17 10:08:55     -512.761963        2.421367\n",
      "BFGS:   18 10:08:55     -513.338928        2.646978\n",
      "BFGS:   19 10:08:55     -513.878418        2.863281\n",
      "BFGS:   20 10:08:55     -514.484192        2.947634\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:55     -391.597900      172.892810\n",
      "BFGS:    1 10:08:55     -445.737457       76.711962\n",
      "BFGS:    2 10:08:55     -467.749023       52.580542\n",
      "BFGS:    3 10:08:55     -484.600922       32.867129\n",
      "BFGS:    4 10:08:56     -494.567047       21.054830\n",
      "BFGS:    5 10:08:56     -500.914764       14.790725\n",
      "BFGS:    6 10:08:56     -504.952606        8.840186\n",
      "BFGS:    7 10:08:56     -507.712799        5.390269\n",
      "BFGS:    8 10:08:56     -509.572113        3.737004\n",
      "BFGS:    9 10:08:56     -510.813690        3.579353\n",
      "BFGS:   10 10:08:56     -512.393494        3.800400\n",
      "BFGS:   11 10:08:57     -513.534729        3.441299\n",
      "BFGS:   12 10:08:57     -514.669983        2.785575\n",
      "BFGS:   13 10:08:57     -516.083008        2.357028\n",
      "BFGS:   14 10:08:57     -516.815918        2.207703\n",
      "BFGS:   15 10:08:57     -517.235718        2.188597\n",
      "BFGS:   16 10:08:57     -518.036255        1.980806\n",
      "BFGS:   17 10:08:57     -518.524109        1.880740\n",
      "BFGS:   18 10:08:58     -518.849670        1.435361\n",
      "BFGS:   19 10:08:58     -519.144226        1.168761\n",
      "BFGS:   20 10:08:58     -519.286072        0.927030\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:08:58    34390.828125   815048.118687\n",
      "BFGS:    1 10:08:58     -334.993988      410.095987\n",
      "BFGS:    2 10:08:58     -393.168060      425.751284\n",
      "BFGS:    3 10:08:58     -335.990204     1510.933652\n",
      "BFGS:    4 10:08:59     -413.388916      634.033245\n",
      "BFGS:    5 10:08:59     -445.624359       66.161830\n",
      "BFGS:    6 10:08:59     -467.338287       38.967741\n",
      "BFGS:    7 10:08:59     -477.144043       62.093573\n",
      "BFGS:    8 10:08:59     -485.778442       52.435633\n",
      "BFGS:    9 10:08:59     -491.361084       20.899352\n",
      "BFGS:   10 10:08:59     -494.239166        6.287089\n",
      "BFGS:   11 10:08:59     -496.248688        7.236671\n",
      "BFGS:   12 10:08:59     -497.986969        5.909947\n",
      "BFGS:   13 10:08:59     -499.316162       15.231950\n",
      "BFGS:   14 10:09:00     -500.255341       10.451403\n",
      "BFGS:   15 10:09:00     -501.935547       16.860282\n",
      "BFGS:   16 10:09:00     -503.435883        9.486502\n",
      "BFGS:   17 10:09:00     -504.213043       15.646570\n",
      "BFGS:   18 10:09:00     -505.447754       14.703298\n",
      "BFGS:   19 10:09:00     -506.422028        4.324044\n",
      "BFGS:   20 10:09:00     -507.241302        2.737947\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:09:00 - mcmc.system | INFO: Optimized structure has surface energy = 5.464\n",
      "10:09:00 - mcmc.mcmc | INFO: In sweep 8 out of 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:00     -364.655273      452.207986\n",
      "BFGS:    1 10:09:01     -263.977264     1740.661297\n",
      "BFGS:    2 10:09:01     -382.196075      452.183038\n",
      "BFGS:    3 10:09:01     -417.586517      279.703666\n",
      "BFGS:    4 10:09:01     -424.826019      110.275503\n",
      "BFGS:    5 10:09:01     -441.257080      126.330045\n",
      "BFGS:    6 10:09:01     -457.463593      109.658521\n",
      "BFGS:    7 10:09:01     -463.516998       50.693660\n",
      "BFGS:    8 10:09:01     -467.305023       37.758848\n",
      "BFGS:    9 10:09:02     -469.223267       19.595089\n",
      "BFGS:   10 10:09:02     -472.218353       11.442482\n",
      "BFGS:   11 10:09:02     -473.447540       59.812696\n",
      "BFGS:   12 10:09:02     -474.774078       24.503242\n",
      "BFGS:   13 10:09:02     -474.125244      117.313649\n",
      "BFGS:   14 10:09:02     -476.471313       57.818260\n",
      "BFGS:   15 10:09:02     -476.371979       86.636267\n",
      "BFGS:   16 10:09:02     -477.179718       19.849765\n",
      "BFGS:   17 10:09:03     -477.516235       13.549435\n",
      "BFGS:   18 10:09:03     -477.750610       44.772245\n",
      "BFGS:   19 10:09:03     -478.117188       29.001332\n",
      "BFGS:   20 10:09:03     -478.355957       26.867100\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:03     -383.519073      173.942081\n",
      "BFGS:    1 10:09:03     -435.642731       75.909373\n",
      "BFGS:    2 10:09:03      205.803696    11246.140316\n",
      "BFGS:    3 10:09:03     -281.590698     3074.647297\n",
      "BFGS:    4 10:09:03     -440.374359      599.315573\n",
      "BFGS:    5 10:09:04     -473.955811      154.667625\n",
      "BFGS:    6 10:09:04     -483.598907       78.582312\n",
      "BFGS:    7 10:09:04     -486.283447       67.056038\n",
      "BFGS:    8 10:09:04     -490.499756        8.897885\n",
      "BFGS:    9 10:09:04     -492.590576        6.271506\n",
      "BFGS:   10 10:09:05     -494.786835        4.571411\n",
      "BFGS:   11 10:09:05     -496.530548        9.569781\n",
      "BFGS:   12 10:09:05     -498.003418       22.438423\n",
      "BFGS:   13 10:09:05     -499.125946       30.751019\n",
      "BFGS:   14 10:09:05     -500.625885       20.918005\n",
      "BFGS:   15 10:09:05     -501.719604        2.585955\n",
      "BFGS:   16 10:09:05     -502.183472       10.234141\n",
      "BFGS:   17 10:09:05     -502.627777       15.312513\n",
      "BFGS:   18 10:09:05     -503.058838       10.786347\n",
      "BFGS:   19 10:09:05     -503.387329        2.953228\n",
      "BFGS:   20 10:09:06     -503.695221       14.041694\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:06     -394.040985      173.713030\n",
      "BFGS:    1 10:09:06     -447.223907       76.496279\n",
      "BFGS:    2 10:09:06     -466.680908       53.049981\n",
      "BFGS:    3 10:09:06     -481.022797       31.376041\n",
      "BFGS:    4 10:09:06     -490.249603       20.596255\n",
      "BFGS:    5 10:09:06     -496.999023       13.722045\n",
      "BFGS:    6 10:09:06     -501.210602        8.910857\n",
      "BFGS:    7 10:09:07     -504.181396        6.472750\n",
      "BFGS:    8 10:09:07     -505.998322        5.340886\n",
      "BFGS:    9 10:09:07     -507.261139        4.743827\n",
      "BFGS:   10 10:09:07     -508.893921        3.750359\n",
      "BFGS:   11 10:09:07     -510.429535        3.681322\n",
      "BFGS:   12 10:09:07     -511.486938        3.455789\n",
      "BFGS:   13 10:09:07     -512.176208        3.249289\n",
      "BFGS:   14 10:09:07     -512.758728        3.169270\n",
      "BFGS:   15 10:09:07     -513.392578        2.921482\n",
      "BFGS:   16 10:09:08     -513.911865        2.436417\n",
      "BFGS:   17 10:09:08     -514.406433        1.807935\n",
      "BFGS:   18 10:09:08     -514.687012        1.473908\n",
      "BFGS:   19 10:09:08     -514.913940        1.396771\n",
      "BFGS:   20 10:09:08     -515.072998        1.410230\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:08     -395.797943      172.991775\n",
      "BFGS:    1 10:09:08     -449.150635       77.132907\n",
      "BFGS:    2 10:09:09     -469.365967       53.404120\n",
      "BFGS:    3 10:09:09     -484.042725       32.461777\n",
      "BFGS:    4 10:09:09     -493.486969       21.049869\n",
      "BFGS:    5 10:09:09     -500.477509       14.239978\n",
      "BFGS:    6 10:09:09     -504.996429        8.593548\n",
      "BFGS:    7 10:09:09     -507.932281        6.493056\n",
      "BFGS:    8 10:09:09     -509.698578        5.309335\n",
      "BFGS:    9 10:09:09     -510.964478        4.523163\n",
      "BFGS:   10 10:09:09     -512.521240        3.949809\n",
      "BFGS:   11 10:09:10     -513.949890        3.301094\n",
      "BFGS:   12 10:09:10     -515.149475        2.608563\n",
      "BFGS:   13 10:09:10     -516.033020        3.280200\n",
      "BFGS:   14 10:09:10     -516.836914        3.088102\n",
      "BFGS:   15 10:09:10     -517.477844        2.470579\n",
      "BFGS:   16 10:09:10     -517.957214        1.872858\n",
      "BFGS:   17 10:09:10     -518.244385        1.580682\n",
      "BFGS:   18 10:09:10     -518.412537        1.275584\n",
      "BFGS:   19 10:09:10     -518.543396        1.120805\n",
      "BFGS:   20 10:09:10     -518.663757        0.956136\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:11     -394.708496      168.375116\n",
      "BFGS:    1 10:09:11     -447.142975       71.371422\n",
      "BFGS:    2 10:09:11     -269.351166     3021.592957\n",
      "BFGS:    3 10:09:11     -456.345306      461.110870\n",
      "BFGS:    4 10:09:11     -485.132690       46.288748\n",
      "BFGS:    5 10:09:11     -492.585358       16.501579\n",
      "BFGS:    6 10:09:11     -498.343872       11.854744\n",
      "BFGS:    7 10:09:11     -502.136383       10.213510\n",
      "BFGS:    8 10:09:12     -504.835724       12.453734\n",
      "BFGS:    9 10:09:12     -506.936768       13.835838\n",
      "BFGS:   10 10:09:12     -508.605133        3.263917\n",
      "BFGS:   11 10:09:12     -509.501068        5.179616\n",
      "BFGS:   12 10:09:12     -510.665314        2.911420\n",
      "BFGS:   13 10:09:12     -511.502716        3.225159\n",
      "BFGS:   14 10:09:12     -512.068848        3.434765\n",
      "BFGS:   15 10:09:12     -512.790344        2.734912\n",
      "BFGS:   16 10:09:13     -513.200500        2.468880\n",
      "BFGS:   17 10:09:13     -513.780945        3.357064\n",
      "BFGS:   18 10:09:13     -514.274475        3.383366\n",
      "BFGS:   19 10:09:13     -514.573547        1.880622\n",
      "BFGS:   20 10:09:13     -515.005920        2.156529\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:09:13 - mcmc.system | INFO: Optimized structure has surface energy = 4.848\n",
      "10:09:13 - mcmc.mcmc | INFO: In sweep 9 out of 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:13     -384.806549      172.550168\n",
      "BFGS:    1 10:09:13     -441.632294       71.639465\n",
      "BFGS:    2 10:09:13     -466.560455       54.850167\n",
      "BFGS:    3 10:09:13     -481.427368       37.542756\n",
      "BFGS:    4 10:09:14     -491.887177       26.456288\n",
      "BFGS:    5 10:09:14     -499.611053       18.383058\n",
      "BFGS:    6 10:09:14     -505.359467       14.300229\n",
      "BFGS:    7 10:09:14     -509.422760       10.495450\n",
      "BFGS:    8 10:09:14     -511.900299        8.076932\n",
      "BFGS:    9 10:09:14     -513.593689        6.202960\n",
      "BFGS:   10 10:09:14     -514.872986        5.347211\n",
      "BFGS:   11 10:09:14     -516.667419        4.345098\n",
      "BFGS:   12 10:09:15     -518.156555        3.091788\n",
      "BFGS:   13 10:09:15     -519.231995        3.218463\n",
      "BFGS:   14 10:09:15     -519.871094        3.051903\n",
      "BFGS:   15 10:09:15     -520.502136        2.352428\n",
      "BFGS:   16 10:09:15     -521.037415        2.418547\n",
      "BFGS:   17 10:09:15     -521.667786        2.723678\n",
      "BFGS:   18 10:09:15     -522.155090        2.148726\n",
      "BFGS:   19 10:09:16     -522.568542        1.095742\n",
      "BFGS:   20 10:09:16     -522.742004        0.921467\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:16     -377.471039      170.090986\n",
      "BFGS:    1 10:09:16     -434.280640       71.349793\n",
      "BFGS:    2 10:09:16     -424.383148      611.130484\n",
      "BFGS:    3 10:09:16     -465.259857      222.789708\n",
      "BFGS:    4 10:09:17     -481.860504       26.989169\n",
      "BFGS:    5 10:09:17     -493.394318       17.883780\n",
      "BFGS:    6 10:09:17     -500.682037       12.276541\n",
      "BFGS:    7 10:09:17     -505.997650        8.393965\n",
      "BFGS:    8 10:09:17     -509.693481        6.508199\n",
      "BFGS:    9 10:09:17     -511.953522        9.943985\n",
      "BFGS:   10 10:09:17     -513.347717        7.003301\n",
      "BFGS:   11 10:09:17     -515.299744        8.476032\n",
      "BFGS:   12 10:09:18     -517.050476        5.845010\n",
      "BFGS:   13 10:09:18     -518.499451        4.911158\n",
      "BFGS:   14 10:09:18     -519.318298        4.178727\n",
      "BFGS:   15 10:09:18     -519.857605        1.870905\n",
      "BFGS:   16 10:09:18     -520.262878        3.353022\n",
      "BFGS:   17 10:09:18     -521.026123        6.907631\n",
      "BFGS:   18 10:09:18     -521.532227        3.503167\n",
      "BFGS:   19 10:09:18     -522.187683        3.148455\n",
      "BFGS:   20 10:09:18     -522.521545       12.371241\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:19     -377.624634      172.629678\n",
      "BFGS:    1 10:09:19     -435.081146       71.372161\n",
      "BFGS:    2 10:09:19     -283.067902     2805.812360\n",
      "BFGS:    3 10:09:19     -432.404694      694.898531\n",
      "BFGS:    4 10:09:19     -477.867920       66.497617\n",
      "BFGS:    5 10:09:19     -491.931305       24.685067\n",
      "BFGS:    6 10:09:20     -501.711517       16.909172\n",
      "BFGS:    7 10:09:20     -508.503571       13.469617\n",
      "BFGS:    8 10:09:20     -512.910950       12.178710\n",
      "BFGS:    9 10:09:20     -515.685486        8.455743\n",
      "BFGS:   10 10:09:20     -517.590881        5.810106\n",
      "BFGS:   11 10:09:20     -518.998962        4.734478\n",
      "BFGS:   12 10:09:20     -520.194275       19.536832\n",
      "BFGS:   13 10:09:20     -521.465393       16.801658\n",
      "BFGS:   14 10:09:20     -522.618103        7.353537\n",
      "BFGS:   15 10:09:20     -523.038513        3.359021\n",
      "BFGS:   16 10:09:21     -523.842590       20.509287\n",
      "BFGS:   17 10:09:21     -524.336182        8.264114\n",
      "BFGS:   18 10:09:21     -524.993225        6.953151\n",
      "BFGS:   19 10:09:21     -525.409607        7.985710\n",
      "BFGS:   20 10:09:21     -526.140625        6.610833\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:21     -386.588043      169.619421\n",
      "BFGS:    1 10:09:21     -443.801178       72.205761\n",
      "BFGS:    2 10:09:21     -469.027954       55.331176\n",
      "BFGS:    3 10:09:22     -484.053314       38.123915\n",
      "BFGS:    4 10:09:22     -494.598724       26.612111\n",
      "BFGS:    5 10:09:22     -502.442749       17.895681\n",
      "BFGS:    6 10:09:22     -508.494629       14.049987\n",
      "BFGS:    7 10:09:22     -512.742859       10.117384\n",
      "BFGS:    8 10:09:22     -515.252380        7.290291\n",
      "BFGS:    9 10:09:22     -516.862793        5.503948\n",
      "BFGS:   10 10:09:23     -517.927063        3.999863\n",
      "BFGS:   11 10:09:23     -519.026245        3.834941\n",
      "BFGS:   12 10:09:23     -520.549744        4.200981\n",
      "BFGS:   13 10:09:23     -521.673889        4.294893\n",
      "BFGS:   14 10:09:23     -522.689514        3.948749\n",
      "BFGS:   15 10:09:23     -523.540955        3.389550\n",
      "BFGS:   16 10:09:23     -524.283630        2.352742\n",
      "BFGS:   17 10:09:23     -525.047668        2.056052\n",
      "BFGS:   18 10:09:24     -525.479370        1.735682\n",
      "BFGS:   19 10:09:24     -525.809021        1.530835\n",
      "BFGS:   20 10:09:24     -526.007568        0.854564\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:24    34375.800781   814888.936452\n",
      "BFGS:    1 10:09:24     -365.113556      348.711826\n",
      "BFGS:    2 10:09:24     -422.149170      350.553268\n",
      "BFGS:    3 10:09:25     -452.394684      326.050957\n",
      "BFGS:    4 10:09:25     -477.960724       65.158982\n",
      "BFGS:    5 10:09:25     -493.193085       26.485628\n",
      "BFGS:    6 10:09:25     -502.176025       18.285570\n",
      "BFGS:    7 10:09:25     -508.164337       14.610219\n",
      "BFGS:    8 10:09:26     -512.394287       10.837066\n",
      "BFGS:    9 10:09:26     -514.996521        8.347932\n",
      "BFGS:   10 10:09:26     -516.962891        6.227268\n",
      "BFGS:   11 10:09:26     -518.263367        4.833015\n",
      "BFGS:   12 10:09:26     -519.428772        3.993314\n",
      "BFGS:   13 10:09:27     -521.048523        2.763208\n",
      "BFGS:   14 10:09:27     -521.637268        3.033024\n",
      "BFGS:   15 10:09:27     -522.111267        2.679516\n",
      "BFGS:   16 10:09:27     -522.572571        2.197651\n",
      "BFGS:   17 10:09:27     -522.995850        2.117997\n",
      "BFGS:   18 10:09:27     -523.683838        2.374247\n",
      "BFGS:   19 10:09:27     -524.200623        2.469827\n",
      "BFGS:   20 10:09:27     -524.548401        2.117823\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:09:27 - mcmc.system | INFO: Optimized structure has surface energy = 2.008\n",
      "10:09:27 - mcmc.mcmc | INFO: In sweep 10 out of 10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:28     -379.142426      169.462310\n",
      "BFGS:    1 10:09:28     -441.629059       71.720507\n",
      "BFGS:    2 10:09:28     -466.319427       62.713868\n",
      "BFGS:    3 10:09:28     -483.670807       42.173800\n",
      "BFGS:    4 10:09:28     -494.683716       29.697294\n",
      "BFGS:    5 10:09:28     -503.180023       20.382905\n",
      "BFGS:    6 10:09:28     -509.180389       14.123597\n",
      "BFGS:    7 10:09:28     -513.549744       10.344283\n",
      "BFGS:    8 10:09:28     -516.434021        6.816918\n",
      "BFGS:    9 10:09:29     -518.114563        4.445232\n",
      "BFGS:   10 10:09:29     -519.261047        3.833210\n",
      "BFGS:   11 10:09:29     -520.438660        3.279362\n",
      "BFGS:   12 10:09:29     -521.948547        2.695413\n",
      "BFGS:   13 10:09:29     -523.234070        2.407184\n",
      "BFGS:   14 10:09:29     -524.084534        1.744955\n",
      "BFGS:   15 10:09:29     -524.605225        1.657558\n",
      "BFGS:   16 10:09:29     -525.069641        1.912309\n",
      "BFGS:   17 10:09:29     -525.410706        2.212478\n",
      "BFGS:   18 10:09:30     -525.956726        1.866139\n",
      "BFGS:   19 10:09:30     -526.219055        1.211728\n",
      "BFGS:   20 10:09:30     -526.515381        1.246828\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:30     -381.101318      170.761301\n",
      "BFGS:    1 10:09:30     -441.181305       71.377822\n",
      "BFGS:    2 10:09:30     -468.694336       56.127223\n",
      "BFGS:    3 10:09:30     -484.342682       36.336138\n",
      "BFGS:    4 10:09:30     -495.562866       25.685150\n",
      "BFGS:    5 10:09:31     -503.633392       18.177432\n",
      "BFGS:    6 10:09:31     -509.605225       13.956586\n",
      "BFGS:    7 10:09:31     -513.703857       10.239944\n",
      "BFGS:    8 10:09:31     -516.267883        7.587383\n",
      "BFGS:    9 10:09:31     -518.054504        6.110675\n",
      "BFGS:   10 10:09:31     -519.513489        5.057596\n",
      "BFGS:   11 10:09:31     -520.987671        4.242677\n",
      "BFGS:   12 10:09:31     -522.708801        3.344358\n",
      "BFGS:   13 10:09:31     -523.788330        3.521335\n",
      "BFGS:   14 10:09:32     -524.441223        3.125917\n",
      "BFGS:   15 10:09:32     -524.999512        2.499503\n",
      "BFGS:   16 10:09:32     -525.617981        2.153904\n",
      "BFGS:   17 10:09:32     -526.140198        2.972735\n",
      "BFGS:   18 10:09:32     -526.666687        2.195221\n",
      "BFGS:   19 10:09:32     -526.962097        1.242277\n",
      "BFGS:   20 10:09:32     -527.136902        0.724365\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:32     -346.849609      447.828546\n",
      "BFGS:    1 10:09:33     -374.883331      148.210148\n",
      "BFGS:    2 10:09:33     -432.697418       74.369679\n",
      "BFGS:    3 10:09:33     -457.706146       54.358542\n",
      "BFGS:    4 10:09:33     -479.810150       38.479813\n",
      "BFGS:    5 10:09:33     -496.540680       26.019458\n",
      "BFGS:    6 10:09:33     -508.104126       18.962882\n",
      "BFGS:    7 10:09:33     -516.534485       13.003401\n",
      "BFGS:    8 10:09:33     -521.846008        9.157668\n",
      "BFGS:    9 10:09:34     -525.224792        7.070172\n",
      "BFGS:   10 10:09:34     -527.571960        5.707906\n",
      "BFGS:   11 10:09:34     -529.452881        4.762637\n",
      "BFGS:   12 10:09:34     -531.448669        3.744101\n",
      "BFGS:   13 10:09:34     -532.994629        3.022043\n",
      "BFGS:   14 10:09:34     -534.075684        3.185028\n",
      "BFGS:   15 10:09:34     -534.348267        3.202729\n",
      "BFGS:   16 10:09:35     -534.819336        2.371526\n",
      "BFGS:   17 10:09:35     -534.989807        1.954514\n",
      "BFGS:   18 10:09:35     -535.398865        1.683779\n",
      "BFGS:   19 10:09:35     -535.664612        1.888895\n",
      "BFGS:   20 10:09:35     -536.089905        2.056865\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:35     -381.487640      165.148956\n",
      "BFGS:    1 10:09:35     -440.662842       70.631243\n",
      "BFGS:    2 10:09:35     -468.173309       55.900254\n",
      "BFGS:    3 10:09:35     -483.961517       37.739098\n",
      "BFGS:    4 10:09:36     -495.518707       27.151028\n",
      "BFGS:    5 10:09:36     -504.033356       18.025794\n",
      "BFGS:    6 10:09:36     -510.478760       12.798983\n",
      "BFGS:    7 10:09:36     -515.190857        9.361981\n",
      "BFGS:    8 10:09:36     -518.119446        6.648975\n",
      "BFGS:    9 10:09:36     -520.054626        5.204557\n",
      "BFGS:   10 10:09:36     -521.534363        3.895901\n",
      "BFGS:   11 10:09:36     -522.789612        3.722036\n",
      "BFGS:   12 10:09:36     -524.442505        3.376004\n",
      "BFGS:   13 10:09:37     -525.376526        2.793087\n",
      "BFGS:   14 10:09:37     -526.114014        2.433997\n",
      "BFGS:   15 10:09:37     -526.597046        2.788683\n",
      "BFGS:   16 10:09:37     -527.324402        2.960094\n",
      "BFGS:   17 10:09:37     -527.651978        2.874497\n",
      "BFGS:   18 10:09:37     -528.202087        2.274307\n",
      "BFGS:   19 10:09:38     -528.487610        1.577368\n",
      "BFGS:   20 10:09:38     -528.755188        1.462506\n",
      "      Step     Time          Energy          fmax\n",
      "BFGS:    0 10:09:38     -382.897858      169.060628\n",
      "BFGS:    1 10:09:38     -443.306152       71.939577\n",
      "BFGS:    2 10:09:38     -471.095856       56.594136\n",
      "BFGS:    3 10:09:38     -486.911499       36.945745\n",
      "BFGS:    4 10:09:39     -498.251556       25.895588\n",
      "BFGS:    5 10:09:39     -506.448761       17.744139\n",
      "BFGS:    6 10:09:39     -512.734314       13.750313\n",
      "BFGS:    7 10:09:39     -517.037903        9.983203\n",
      "BFGS:    8 10:09:39     -519.630127        7.046731\n",
      "BFGS:    9 10:09:40     -521.305176        5.458162\n",
      "BFGS:   10 10:09:40     -522.563782        4.035085\n",
      "BFGS:   11 10:09:40     -523.707947        3.488719\n",
      "BFGS:   12 10:09:40     -525.187744        3.170438\n",
      "BFGS:   13 10:09:40     -526.097351        2.768997\n",
      "BFGS:   14 10:09:40     -526.627380        2.350544\n",
      "BFGS:   15 10:09:40     -527.126648        1.958201\n",
      "BFGS:   16 10:09:40     -527.691223        2.043376\n",
      "BFGS:   17 10:09:40     -528.276367        2.504107\n",
      "BFGS:   18 10:09:41     -528.877930        2.009375\n",
      "BFGS:   19 10:09:41     -529.405212        1.234088\n",
      "BFGS:   20 10:09:41     -529.668823        1.476728\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:09:41 - mcmc.system | INFO: Optimized structure has surface energy = 2.443\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time taken = 158.301 seconds\n"
     ]
    }
   ],
   "source": [
    "mcmc = MCMC(**sampling_settings)\n",
    "\n",
    "start = perf_counter()\n",
    "results = mcmc.run(\n",
    "    surface=surface,\n",
    "    **sampling_settings,\n",
    ")\n",
    "stop = perf_counter()\n",
    "print(f\"Time taken = {stop - start:.3f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 3000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_summary_stats(\n",
    "    results[\"energy_hist\"],\n",
    "    results[\"frac_accept_hist\"],\n",
    "    results[\"adsorption_count_hist\"],\n",
    "    sampling_settings[\"total_sweeps\"],\n",
    "    save_folder=run_folder,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save structures for later use in latent space clustering or analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "structures = results[\"history\"]\n",
    "with open(\"data/SrTiO3_001/SrTiO3_001_2x2_mcmc_structures.pkl\", \"wb\") as f:\n",
    "    pickle.dump(structures, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('surface_sampling': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e3e0723b7fd9866ee8f9ae4f62931968cf8456ef2195b337b8930ae9f61708cf"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
